The present invention relates generally to detection methods and apparatus and, more particularly, to methods and apparatus for detecting an abnormality within a host medium.
Imaging systems are widely utilized to construct an image or model of a structure which is otherwise unobservable to the eye. Typically, imaging systems are designed to detect abnormalities, foreign objects or other structures which are embedded within a host medium and which alter or perturb the signal propagation properties of the host medium. For example, x-ray tomography and other medical imaging techniques are commonly used to create an image of a portion of the human body such that tumors or other inclusions can be detected. Similarly, imaging systems have been developed to detect deposits of oil or other minerals within the earth or to detect mines, such as mines buried underground or at sea.
Regardless of the application, conventional imaging systems introduce signals into the host medium and create an image of the host medium and abnormalities within the host medium based upon the interaction of the signals with the host medium and the abnormalities. Typically, an abnormality embedded within a host medium can be identified based upon differences in the signal propagation properties of the host medium and the abnormality. The host medium, as well as any embedded abnormalities, can be defined by a variety of signal propagation properties, such as the diffusion coefficient, the absorption coefficient, the speed of sound, etc. For example, tumors will generally have different signal propagation properties than the surrounding tissue. As a result of these signal propagation properties, the signals introduced by conventional imaging systems into a host medium will be scattered and absorbed in a manner dictated by the respective signal propagation properties of the host medium and any abnormalities within the host medium. Likewise, the signals introduced into a host medium will generally be detected at different times as a result of differences in the relative speed of sound within the host medium and any abnormalities within the host medium.
Conventional imaging systems have typically detected and analyzed the intensity of the signals which had propagated through the host medium in order to create an image of the host medium and any abnormalities within the host medium. Conventional imaging systems have generally treated scattered signals as noise, however, and discarded any information contained by the scattered signals regarding the host medium or any abnormalities within the host medium due to the difficulty of creating an accurate and fast algorithm to describe the behavior of the scattered signals and the excessive computational time required to process the scattered signals by conventional techniques. As used herein, signals which do not propagate along a straight line or along a predefined curve will generally be considered to be scattered.
Although a number of imaging systems are commercially available, these conventional imaging systems do not attempt to provide robust images of the host medium and the abnormalities within the host medium in an efficient, timely and cost-effective manner by the use of scattered signals. One example where the use of scattered signals would be beneficial is during the detection and analysis of cancerous tumors which typically absorb more light than pre-malignant tissues. This is because cancerous tumors are typically of a relatively low absorbing contrast and are thus not readily detectable by x-ray tomography at early stages of development. Another example is provided by imaging systems which discard the scattered signals as noise and rely upon the reflected signals. As a result, these imaging systems have difficulty in detecting abnormalities within a turbid medium since a majority of these signals are scattered and the energy of the unscattered signals, i.e., the ballistic photons, is quite small.
Accordingly, a number of imaging techniques have been developed which collect and evaluate at least a portion of the scattered signals. For example, an article by Barbour, et al., entitled "Mapping of Photon Distribution and Imaging of MR-Derived Anatomically Accurate Optical Models of the Female Breast", SPIE Proceedings, Vol. 2389 (1995) describes an imaging algorithm for creating an image of a breast based upon a previously measured photon distribution. In addition, an article by Das, et al., entitled "Analysis of Time-Resolved Data for Tomographical Image Reconstruction of Opaque Phantoms and Finite Absorbers in Diffusive Media", SPIE Proceedings, Vol. 2389 (1995) describes an imaging system which detects diffusely scattered light and which evaluates the detected light according to another imaging algorithm in order to create an image of the diffusive media. As set forth in these articles, light is introduced from a number of light sources into the test object and the scattered light emerging from the test object is detected. Based upon the detected light, the iterative imaging algorithms described by these articles reconstruct an image of the test object. These iterative algorithms are based on a perturbation model and include a projection onto convex sets algorithm, a conjugate gradient descent algorithm and a simultaneous algebraic reconstruction algorithm.
U.S. Pat. No. 5,070,455 to Jerome R. Singer, et al., also describes an imaging system which generates images of the interior of a test object based upon an analysis of radiation which has been attenuated and scattered by the test object. According to the Singer imaging system, the interior of the test object is modeled as an array of volume elements or voxels. The optical properties of each voxel and, in particular, the scattering and attenuation properties of each voxel are then specified based upon preassigned numerical coefficients.
In operation, the Singer imaging system radiates a test object at a number of points near the exterior of the test object and measures the radiation emerging from the test object at a number of exit points near the exterior of the test object. The imaging system also computes the theoretical intensity of the radiation which would emerge from the test object at each of the exit points if the interior of the test object had the scattering and attenuation properties which are specified for the plurality of voxels. Thereafter, the Singer imaging system determines the difference between the actual intensity of the radiation as measured and the theoretical intensity as computed to determine the magnitude of error therebetween. Based upon a gradient descent methodology, the error function is then minimized by modifying the numerical coefficients representative of the scattering and attenuation properties of each of the voxels. In particular, the numerical coefficients associated with each voxel are iteratively updated until the error function falls below a predetermined threshold value. Based upon the set of numerical coefficients which minimize the error function between the actual intensity and the theoretical intensity, the Singer imaging system generates a series of images of the interior of the test object. See also U.S. Pat. No. 5,137,355 to Randall L. Barbour, et al.
As described above, several imaging systems have been developed to detect and analyze scattered signals in an attempt to overcome the deficiencies associated with conventional imaging systems which discard scattered signals and which rely upon the reflected signals to create an image of the host medium. However, the imaging systems which have been developed to detect and analyze scattered signals, such as those described above, are typically quite computationally intensive and, as a result, may require significant computational time and/or computational resources in order to analyze the scattered signals. For example, the iterative algorithms proposed by the Barbour and Das articles typically require one or more ill-conditioned matrices having many nonzero entries to be solved, thereby significantly increasing the computational time of these imaging systems.